prikaz prve stranice dokumenta Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks
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Scientific paper - Original scientific paper
Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks
Separation Science and Technology, 40 (2005), 6; 1333-1352. https://doi.org/10.1081/SS-200052816
Bolanča, Tomislav; Cerjan-Stefanović, Štefica; Srečnik, Goran; Debeljak, Željko; Novič, Milko

Institutional repository: Repository UHC Osijek

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Bolanča, T., Cerjan Stefanović, Š., Srečnik, G., Debeljak, Ž. & Novič, M. (2005). Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks. Separation Science and Technology, 40. (6), 1333-1352. doi: 10.1081/SS-200052816

Bolanča, Tomislav, et al. "Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks." Separation Science and Technology, vol. 40, no. 6, 2005, pp. 1333-1352. https://doi.org/10.1081/SS-200052816

Bolanča, Tomislav, Štefica Cerjan Stefanović, Goran Srečnik, Željko Debeljak and Milko Novič. "Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks." Separation Science and Technology 40, no. 6 (2005): 1333-1352. https://doi.org/10.1081/SS-200052816

Bolanča, T., et al. (2005) 'Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks', Separation Science and Technology, 40(6), pp. 1333-1352. doi: 10.1081/SS-200052816

Bolanča T, Cerjan Stefanović Š, Srečnik G, Debeljak Ž, Novič M. Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks. Separation Science and Technology [Internet]. 2005 [cited 2024 May 16];40(6):1333-1352. doi: 10.1081/SS-200052816

T. Bolanča, Š. Cerjan Stefanović, G. Srečnik, Ž. Debeljak and M. Novič, "Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks", Separation Science and Technology, vol. 40, no. 6, pp. 1333-1352, 2005. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:239:911779. [Accessed: 16 May 2024]

Metadata
Title (english)Comparison of Retention Modeling in ion Chromatography by Using Multiple Linear Regression and Artificial Neural Networks
AuthorTomislav Bolanča
MBZ: 233894
AuthorŠtefica Cerjan-Stefanović
MBZ: 6631
AuthorGoran Srečnik
AuthorŽeljko Debeljak
MBZ: 296571
AuthorMilko Novič
Author's institution(Clinical hospital center Osijek)
Author's institutionUniversity of Zagreb
Faculty of Chemical Engineering and Technology
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Neurology
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Pharmacy
Pharmacy
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Pharmacy
Medical Biochemistry
Abstract (english)
Abstract: The aim of this work is comparison of the prediction power of multiple linear regression and artificial neural networks retention models for inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) in suppressed ion chromatography with isocratic elution. Relations between ion chromatographic parameters (eluent flow rate and concentration of OH2 in eluent) and retention time of particular anion are described with unique mathematical function obtained by multiple linear regression and with a three-layers feed-forward artificial neural network. The artificial neural network was trained with a Levenberg-Marquardt batch error back propagation algorithm. It is shown that the multiple linear regression retention model has lower, but still very satisfactory, predictive ability. Due to its complexity, the artificial neural network must still be regarded as a more complicated technique. That indicates multiple linear regression as a method of choice for retention modeling in the case of ion chromatographic analysis with isocratic elution.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleSeparation Science and Technology
Numberingvol. 40, no. 6, pp. 1333-1352
p-ISSN0149-6395
e-ISSN1520-5754
DOIhttps://doi.org/10.1081/SS-200052816
URN:NBNurn:nbn:hr:239:911779
Publication2005
Document URLhttps://www.tandfonline.com/doi/full/10.1081/SS-200052816
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 3.0 Unported (CC BY 3.0) icon
Created on2020-05-11 10:37:47